@inproceedings{alderete-sellami-2026-structured,
title = "Structured Partial Predictability in Non-Concatenative Morphology: The Case of Tashlhiyt {B}erber",
author = "Alderete, John and
Sellami, Hamza",
editor = "Hinrichs, Erhard and
Nivre, Joakim and
Osenova, Petya and
Pustejovsky, James and
Zinn, Claus",
booktitle = "Proceedings of the Workshop on Structured Linguistic Data and Evaluation ({SL}i{DE})",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.slide-1.11/",
doi = "10.63317/32kvdo5hchjo",
pages = "124--135",
abstract = "Non-concatenative morphology poses a persistent challenge for NLP, yet structured quantitative resources for Amazigh (Berber) languages remain scarce. We present the first large-scale computational study of Tashlhiyt Berber plural formation, drawing on a richly annotated dataset of 1,185 noun paradigms with phonological, morphological and semantic features. We decompose the plural system into macro-level word-formation strategies and micro-level stem mutations, and evaluate predictability across ten target domains using linguistic feature models, N-gram baselines, and Bi-LSTM neural models. Results reveal a structured split: linguistic features decisively outperform neural models on systematic macro-level strategies (e.g., +44.5pp F1), while Bi-LSTMs better capture lexically idiosyncratic patterns. Rather than supporting a categorical rule/memory divide, this complementarity reveals gradient layers of regularity within a single morphological system. These findings demonstrate the value of linguistically informed annotation for probing morphological complexity in low-resource, typologically diverse languages. All data, code, and models are publicly available."
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<abstract>Non-concatenative morphology poses a persistent challenge for NLP, yet structured quantitative resources for Amazigh (Berber) languages remain scarce. We present the first large-scale computational study of Tashlhiyt Berber plural formation, drawing on a richly annotated dataset of 1,185 noun paradigms with phonological, morphological and semantic features. We decompose the plural system into macro-level word-formation strategies and micro-level stem mutations, and evaluate predictability across ten target domains using linguistic feature models, N-gram baselines, and Bi-LSTM neural models. Results reveal a structured split: linguistic features decisively outperform neural models on systematic macro-level strategies (e.g., +44.5pp F1), while Bi-LSTMs better capture lexically idiosyncratic patterns. Rather than supporting a categorical rule/memory divide, this complementarity reveals gradient layers of regularity within a single morphological system. These findings demonstrate the value of linguistically informed annotation for probing morphological complexity in low-resource, typologically diverse languages. All data, code, and models are publicly available.</abstract>
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%0 Conference Proceedings
%T Structured Partial Predictability in Non-Concatenative Morphology: The Case of Tashlhiyt Berber
%A Alderete, John
%A Sellami, Hamza
%Y Hinrichs, Erhard
%Y Nivre, Joakim
%Y Osenova, Petya
%Y Pustejovsky, James
%Y Zinn, Claus
%S Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F alderete-sellami-2026-structured
%X Non-concatenative morphology poses a persistent challenge for NLP, yet structured quantitative resources for Amazigh (Berber) languages remain scarce. We present the first large-scale computational study of Tashlhiyt Berber plural formation, drawing on a richly annotated dataset of 1,185 noun paradigms with phonological, morphological and semantic features. We decompose the plural system into macro-level word-formation strategies and micro-level stem mutations, and evaluate predictability across ten target domains using linguistic feature models, N-gram baselines, and Bi-LSTM neural models. Results reveal a structured split: linguistic features decisively outperform neural models on systematic macro-level strategies (e.g., +44.5pp F1), while Bi-LSTMs better capture lexically idiosyncratic patterns. Rather than supporting a categorical rule/memory divide, this complementarity reveals gradient layers of regularity within a single morphological system. These findings demonstrate the value of linguistically informed annotation for probing morphological complexity in low-resource, typologically diverse languages. All data, code, and models are publicly available.
%R 10.63317/32kvdo5hchjo
%U https://aclanthology.org/2026.slide-1.11/
%U https://doi.org/10.63317/32kvdo5hchjo
%P 124-135
Markdown (Informal)
[Structured Partial Predictability in Non-Concatenative Morphology: The Case of Tashlhiyt Berber](https://aclanthology.org/2026.slide-1.11/) (Alderete & Sellami, SLiDE 2026)
ACL